Health maintenance facility system effectiveness testing

The Medical Simulations Working Group conducted a series of medical simulations to evaluate the proposed Health Maintenance Facility (HMF) Preliminary Design Review (PDR) configuration. The goal of these simulations was to test the system effectiveness of the HMF PDR configurations. The objectives of the medical simulations are to (1) ensure fulfillment of requirements with this HMF design, (2) demonstrate the conformance of the system to human engineering design criteria, and (3) determine whether undesirable design or procedural features were introduced into the design. The simulations consisted of performing 6 different medical scenarios with the HMF mockup in the KRUG laboratory. The scenarios included representative medical procedures and used a broad spectrum of HMF equipment and supplies. Scripts were written and simulations performed by medical simulations working group members under observation from others. Data were collected by means of questionnaires, debriefings, and videotapes. Results were extracted and listed in the individual reports. Specific issues and recommendations from each simulation were compiled into the individual reports. General issues regarding the PDR design of the HMF are outlined in the summary report

A gene signature for post-infectious chronic fatigue syndrome

Background: At present, there are no clinically reliable disease markers for chronic fatigue syndrome. DNA chip microarray technology provides a method for examining the differential expression of mRNA from a large number of genes. Our hypothesis was that a gene expression signature, generated by microarray assays, could help identify genes which are dysregulated in patients with post-infectious CFS and so help identify biomarkers for the condition. Methods: Human genome-wide Affymetrix GeneChip arrays (39,000 transcripts derived from 33,000 gene sequences) were used to compare the levels of gene expression in the peripheral blood mononuclear cells of male patients with post-infectious chronic fatigue (n = 8) and male healthy control subjects (n = 7). Results: Patients and healthy subjects differed significantly in the level of expression of 366 genes. Analysis of the differentially expressed genes indicated functional implications in immune modulation, oxidative stress and apoptosis. Prototype biomarkers were identified on the basis of differential levels of gene expression and possible biological significance Conclusion: Differential expression of key genes identified in this study offer an insight into the possible mechanism of chronic fatigue following infection. The representative biomarkers identified in this research appear promising as potential biomarkers for diagnosis and treatment

Proceedings of the 2015 Asian American/Pacific Islander Nurses Association conference: Biological measures: A role in nursing self-management research

N/

Southwest Research Institute assistance to NASA in biomedical areas of the technology

Significant applications of aerospace technology were achieved. These applications include: a miniaturized, noninvasive system to telemeter electrocardiographic signals of heart transplant patients during their recuperative period as graded situations are introduced; and economical vital signs monitor for use in nursing homes and rehabilitation hospitals to indicate the onset of respiratory arrest; an implantable telemetry system to indicate the onset of the rejection phenomenon in animals undergoing cardiac transplants; an exceptionally accurate current proportional temperature controller for pollution studies; an automatic, atraumatic blood pressure measurement device; materials for protecting burned areas in contact with joint bender splints; a detector to signal the passage of animals by a given point during ecology studies; and special cushioning for use with below-knee amputees to protect the integrity of the skin at the stump/prosthesis interface

Coxsackievirus A6 strains causing an outbreak of hand-foot-and-mouth disease in Northeastern Brazil in 2018

Hand-foot-and-mouth disease (HFMD) is a highly contagious viral disease commonly associated to Enteroviruses (EV). During 2018, Brazil faced massive HFMD outbreaks spread across the country. This study aimed to characterize the EV responsible for the HFMD outbreak that occurred in Paraiba State, Brazilian Northeastern region, in 2018, followed by a phylogenetic analysis to detail information on its genetic diversity. A total of 49 serum samples (one from each patient) collected from children ≤ 15 years old, clinically diagnosed with HFMD were tested for EV using conventional RT-PCR and RT-qPCR. EV infection was confirmed in 71.4% (35/49) of samples. The mean and median ages were 1.83 years and one year old, respectively. Twenty-two EV-positive samples were successfully sequenced and classified as EV-A species; 13 samples were also identified with the CV-A6 genotype. The phylogenetic analysis (VP1 region) of three samples revealed that the detected CV-A6 strains belonged to sub-lineage D3. The CV-A6 strains detected here clustered with strains from South America, Europe and West Asia strains that were also involved in HFMD cases during the 2017-2018 seasons, in addition to the previously detected Brazilian CV-A6 strains from 2012 to 2017, suggesting a global co-circulation of a set of different CV-A6 strains introduced in the country at different times. The growing circulation of the emerging CV-A6 associated with HFMD, together with the detection of more severe cases worldwide, suggests the need for a more intense surveillance system of HFMD in Brazil. In addition, this investigation was performed exclusively on serum samples, and the analysis of whole blood samples should be considered and could have shown advantages when employed in the diagnosis of enteroviral HFMD outbreaks

Diabetes Prediction Using Artificial Neural Network

Diabetes is one of the most common diseases worldwide where a cure is not found for it yet. Annually it cost a lot of money to care for people with diabetes. Thus the most important issue is the prediction to be very accurate and to use a reliable method for that. One of these methods is using artificial intelligence systems and in particular is the use of Artificial Neural Networks (ANN). So in this paper, we used artificial neural networks to predict whether a person is diabetic or not. The criterion was to minimize the error function in neural network training using a neural network model. After training the ANN model, the average error function of the neural network was equal to 0.01 and the accuracy of the prediction of whether a person is diabetics or not was 87.3